Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations17183
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory675.1 B

Variable types

Numeric5
DateTime1
Categorical11
Text4

Alerts

is_wicket has constant value "1" Constant
extras is highly overall correlated with wideHigh correlation
match_no is highly overall correlated with stageHigh correlation
over is highly overall correlated with over_numberHigh correlation
over_number is highly overall correlated with overHigh correlation
stage is highly overall correlated with match_no and 1 other fieldsHigh correlation
venue is highly overall correlated with stageHigh correlation
wide is highly overall correlated with extrasHigh correlation
stage is highly imbalanced (70.6%) Imbalance
wide is highly imbalanced (76.6%) Imbalance
legbyes is highly imbalanced (90.6%) Imbalance
byes is highly imbalanced (98.3%) Imbalance
noballs is highly imbalanced (96.3%) Imbalance
wicket_type is highly imbalanced (88.6%) Imbalance
runs_of_bat has 6253 (36.4%) zeros Zeros
extras has 16218 (94.4%) zeros Zeros
over_number has 924 (5.4%) zeros Zeros

Reproduction

Analysis started2025-06-13 15:33:41.773638
Analysis finished2025-06-13 15:33:52.069805
Duration10.3 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_no
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.110924
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-06-13T21:03:52.328265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median37
Q356
95-th percentile71
Maximum74
Range73
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.460771
Coefficient of variation (CV)0.57828717
Kurtosis-1.2029432
Mean37.110924
Median Absolute Deviation (MAD)18
Skewness0.033874446
Sum637677
Variance460.56469
MonotonicityIncreasing
2025-06-13T21:03:52.721926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 261
 
1.5%
32 260
 
1.5%
23 259
 
1.5%
21 258
 
1.5%
48 256
 
1.5%
9 256
 
1.5%
64 254
 
1.5%
22 254
 
1.5%
42 253
 
1.5%
54 253
 
1.5%
Other values (63) 14619
85.1%
ValueCountFrequency (%)
1 223
1.3%
2 261
1.5%
3 243
1.4%
4 246
1.4%
5 252
1.5%
6 232
1.4%
7 230
1.3%
8 250
1.5%
9 256
1.5%
10 217
1.3%
ValueCountFrequency (%)
74 252
1.5%
73 244
1.4%
72 247
1.4%
71 147
0.9%
70 244
1.4%
69 244
1.4%
68 246
1.4%
67 242
1.4%
66 249
1.4%
65 247
1.4%

date
Date

Distinct61
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size134.4 KiB
Minimum2025-03-22 00:00:00
Maximum2025-06-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-13T21:03:53.050009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:53.393795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
League stage
16293 
Playoffs stage
 
890

Length

Max length14
Median length12
Mean length12.103591
Min length12

Characters and Unicode

Total characters207976
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeague stage
2nd rowLeague stage
3rd rowLeague stage
4th rowLeague stage
5th rowLeague stage

Common Values

ValueCountFrequency (%)
League stage 16293
94.8%
Playoffs stage 890
 
5.2%

Length

2025-06-13T21:03:53.764441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:03:54.023477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
stage 17183
50.0%
league 16293
47.4%
playoffs 890
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 49769
23.9%
a 34366
16.5%
g 33476
16.1%
s 18073
 
8.7%
17183
 
8.3%
t 17183
 
8.3%
L 16293
 
7.8%
u 16293
 
7.8%
f 1780
 
0.9%
P 890
 
0.4%
Other values (3) 2670
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173610
83.5%
Space Separator 17183
 
8.3%
Uppercase Letter 17183
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49769
28.7%
a 34366
19.8%
g 33476
19.3%
s 18073
 
10.4%
t 17183
 
9.9%
u 16293
 
9.4%
f 1780
 
1.0%
l 890
 
0.5%
y 890
 
0.5%
o 890
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
L 16293
94.8%
P 890
 
5.2%
Space Separator
ValueCountFrequency (%)
17183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190793
91.7%
Common 17183
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49769
26.1%
a 34366
18.0%
g 33476
17.5%
s 18073
 
9.5%
t 17183
 
9.0%
L 16293
 
8.5%
u 16293
 
8.5%
f 1780
 
0.9%
P 890
 
0.5%
l 890
 
0.5%
Other values (2) 1780
 
0.9%
Common
ValueCountFrequency (%)
17183
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49769
23.9%
a 34366
16.5%
g 33476
16.1%
s 18073
 
8.7%
17183
 
8.3%
t 17183
 
8.3%
L 16293
 
7.8%
u 16293
 
7.8%
f 1780
 
0.9%
P 890
 
0.4%
Other values (3) 2670
 
1.3%

venue
Categorical

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Narendra Modi Stadium, Ahmedabad
2257 
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
1951 
Arun Jaitley Stadium, Delhi
1719 
Sawai Mansingh Stadium, Jaipur
1669 
Wankhede Stadium, Mumbai
1627 
Other values (9)
7960 

Length

Max length82
Median length66
Mean length39.596287
Min length21

Characters and Unicode

Total characters680383
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEden Gardens, Kolkata
2nd rowEden Gardens, Kolkata
3rd rowEden Gardens, Kolkata
4th rowEden Gardens, Kolkata
5th rowEden Gardens, Kolkata

Common Values

ValueCountFrequency (%)
Narendra Modi Stadium, Ahmedabad 2257
13.1%
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 1951
11.4%
Arun Jaitley Stadium, Delhi 1719
10.0%
Sawai Mansingh Stadium, Jaipur 1669
9.7%
Wankhede Stadium, Mumbai 1627
9.5%
Eden Gardens, Kolkata 1582
9.2%
MA Chidambaram Stadium, Chennai 1426
8.3%
Rajiv Gandhi International Stadium, Hyderabad 1305
7.6%
M.Chinnaswamy Stadium, Bengaluru 1125
6.5%
Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, Chandigarh 1077
6.3%
Other values (4) 1445
8.4%

Length

2025-06-13T21:03:54.289141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium 15601
 
18.3%
cricket 4473
 
5.2%
international 2629
 
3.1%
narendra 2257
 
2.6%
modi 2257
 
2.6%
ahmedabad 2257
 
2.6%
ratna 1951
 
2.3%
shri 1951
 
2.3%
atal 1951
 
2.3%
bihari 1951
 
2.3%
Other values (41) 48032
56.3%

Most occurring characters

ValueCountFrequency (%)
a 101951
15.0%
68127
 
10.0%
i 53148
 
7.8%
d 39912
 
5.9%
n 39389
 
5.8%
t 35684
 
5.2%
r 33987
 
5.0%
e 30656
 
4.5%
u 27947
 
4.1%
h 25870
 
3.8%
Other values (35) 223712
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 499670
73.4%
Uppercase Letter 91102
 
13.4%
Space Separator 68127
 
10.0%
Other Punctuation 21021
 
3.1%
Dash Punctuation 463
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 101951
20.4%
i 53148
10.6%
d 39912
 
8.0%
n 39389
 
7.9%
t 35684
 
7.1%
r 33987
 
6.8%
e 30656
 
6.1%
u 27947
 
5.6%
h 25870
 
5.2%
m 24431
 
4.9%
Other values (12) 86695
17.4%
Uppercase Letter
ValueCountFrequency (%)
S 21008
23.1%
M 10752
11.8%
C 10700
11.7%
A 8995
9.9%
B 5509
 
6.0%
R 4182
 
4.6%
E 3533
 
3.9%
J 3388
 
3.7%
G 3369
 
3.7%
D 2898
 
3.2%
Other values (9) 16768
18.4%
Other Punctuation
ValueCountFrequency (%)
, 18507
88.0%
. 2514
 
12.0%
Space Separator
ValueCountFrequency (%)
68127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 590772
86.8%
Common 89611
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 101951
17.3%
i 53148
 
9.0%
d 39912
 
6.8%
n 39389
 
6.7%
t 35684
 
6.0%
r 33987
 
5.8%
e 30656
 
5.2%
u 27947
 
4.7%
h 25870
 
4.4%
m 24431
 
4.1%
Other values (31) 177797
30.1%
Common
ValueCountFrequency (%)
68127
76.0%
, 18507
 
20.7%
. 2514
 
2.8%
- 463
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 680383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 101951
15.0%
68127
 
10.0%
i 53148
 
7.8%
d 39912
 
5.9%
n 39389
 
5.8%
t 35684
 
5.2%
r 33987
 
5.0%
e 30656
 
4.5%
u 27947
 
4.1%
h 25870
 
3.8%
Other values (35) 223712
32.9%

batting_team
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size867.7 KiB
PBKS
1965 
MI
1881 
GT
1832 
CSK
1743 
LSG
1741 
Other values (5)
8021 

Length

Max length4
Median length3
Mean length2.7038352
Min length2

Characters and Unicode

Total characters46460
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKKR
2nd rowKKR
3rd rowKKR
4th rowKKR
5th rowKKR

Common Values

ValueCountFrequency (%)
PBKS 1965
11.4%
MI 1881
10.9%
GT 1832
10.7%
CSK 1743
10.1%
LSG 1741
10.1%
RCB 1712
10.0%
RR 1674
9.7%
DC 1667
9.7%
SRH 1596
9.3%
KKR 1372
8.0%

Length

2025-06-13T21:03:54.595589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:03:54.908133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 1965
11.4%
mi 1881
10.9%
gt 1832
10.7%
csk 1743
10.1%
lsg 1741
10.1%
rcb 1712
10.0%
rr 1674
9.7%
dc 1667
9.7%
srh 1596
9.3%
kkr 1372
8.0%

Most occurring characters

ValueCountFrequency (%)
R 8028
17.3%
S 7045
15.2%
K 6452
13.9%
C 5122
11.0%
B 3677
7.9%
G 3573
7.7%
P 1965
 
4.2%
M 1881
 
4.0%
I 1881
 
4.0%
T 1832
 
3.9%
Other values (3) 5004
10.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46460
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 8028
17.3%
S 7045
15.2%
K 6452
13.9%
C 5122
11.0%
B 3677
7.9%
G 3573
7.7%
P 1965
 
4.2%
M 1881
 
4.0%
I 1881
 
4.0%
T 1832
 
3.9%
Other values (3) 5004
10.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 46460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 8028
17.3%
S 7045
15.2%
K 6452
13.9%
C 5122
11.0%
B 3677
7.9%
G 3573
7.7%
P 1965
 
4.2%
M 1881
 
4.0%
I 1881
 
4.0%
T 1832
 
3.9%
Other values (3) 5004
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 8028
17.3%
S 7045
15.2%
K 6452
13.9%
C 5122
11.0%
B 3677
7.9%
G 3573
7.7%
P 1965
 
4.2%
M 1881
 
4.0%
I 1881
 
4.0%
T 1832
 
3.9%
Other values (3) 5004
10.8%

bowling_team
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size867.6 KiB
MI
1919 
PBKS
1865 
GT
1837 
RR
1753 
RCB
1736 
Other values (5)
8073 

Length

Max length4
Median length3
Mean length2.6946401
Min length2

Characters and Unicode

Total characters46302
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRCB
3rd rowRCB
4th rowRCB
5th rowRCB

Common Values

ValueCountFrequency (%)
MI 1919
11.2%
PBKS 1865
10.9%
GT 1837
10.7%
RR 1753
10.2%
RCB 1736
10.1%
LSG 1700
9.9%
CSK 1634
9.5%
SRH 1633
9.5%
DC 1603
9.3%
KKR 1503
8.7%

Length

2025-06-13T21:03:55.314337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:03:55.642405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mi 1919
11.2%
pbks 1865
10.9%
gt 1837
10.7%
rr 1753
10.2%
rcb 1736
10.1%
lsg 1700
9.9%
csk 1634
9.5%
srh 1633
9.5%
dc 1603
9.3%
kkr 1503
8.7%

Most occurring characters

ValueCountFrequency (%)
R 8378
18.1%
S 6832
14.8%
K 6505
14.0%
C 4973
10.7%
B 3601
7.8%
G 3537
7.6%
M 1919
 
4.1%
I 1919
 
4.1%
P 1865
 
4.0%
T 1837
 
4.0%
Other values (3) 4936
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46302
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 8378
18.1%
S 6832
14.8%
K 6505
14.0%
C 4973
10.7%
B 3601
7.8%
G 3537
7.6%
M 1919
 
4.1%
I 1919
 
4.1%
P 1865
 
4.0%
T 1837
 
4.0%
Other values (3) 4936
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 46302
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 8378
18.1%
S 6832
14.8%
K 6505
14.0%
C 4973
10.7%
B 3601
7.8%
G 3537
7.6%
M 1919
 
4.1%
I 1919
 
4.1%
P 1865
 
4.0%
T 1837
 
4.0%
Other values (3) 4936
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 8378
18.1%
S 6832
14.8%
K 6505
14.0%
C 4973
10.7%
B 3601
7.8%
G 3537
7.6%
M 1919
 
4.1%
I 1919
 
4.1%
P 1865
 
4.0%
T 1837
 
4.0%
Other values (3) 4936
10.7%

innings
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
1
9027 
2
8146 
3
 
6
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

Length

2025-06-13T21:03:56.023203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:03:56.273195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9027
52.5%
2 8146
47.4%
3 6
 
< 0.1%
4 4
 
< 0.1%

over
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.497032
Minimum0.1
Maximum19.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-06-13T21:03:56.563957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q14.5
median9.4
Q314.3
95-th percentile18.4
Maximum19.6
Range19.5
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation5.6511039
Coefficient of variation (CV)0.59503895
Kurtosis-1.1724849
Mean9.497032
Median Absolute Deviation (MAD)4.9
Skewness0.044332178
Sum163187.5
Variance31.934975
MonotonicityNot monotonic
2025-06-13T21:03:56.892082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 158
 
0.9%
0.1 156
 
0.9%
0.4 155
 
0.9%
4.1 155
 
0.9%
1.2 154
 
0.9%
3.6 154
 
0.9%
0.2 154
 
0.9%
12.5 153
 
0.9%
5.5 153
 
0.9%
7.4 152
 
0.9%
Other values (110) 15639
91.0%
ValueCountFrequency (%)
0.1 156
0.9%
0.2 154
0.9%
0.3 152
0.9%
0.4 155
0.9%
0.5 158
0.9%
0.6 149
0.9%
1.1 150
0.9%
1.2 154
0.9%
1.3 150
0.9%
1.4 152
0.9%
ValueCountFrequency (%)
19.6 96
0.6%
19.5 102
0.6%
19.4 100
0.6%
19.3 102
0.6%
19.2 114
0.7%
19.1 114
0.7%
18.6 112
0.7%
18.5 116
0.7%
18.4 119
0.7%
18.3 123
0.7%
Distinct168
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size990.8 KiB
2025-06-13T21:03:57.835511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.035966
Min length4

Characters and Unicode

Total characters172448
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st rowde Kock
2nd rowde Kock
3rd rowde Kock
4th rowde Kock
5th rowde Kock
ValueCountFrequency (%)
sharma 543
 
2.0%
sai 519
 
1.9%
sudharsan 509
 
1.9%
iyer 466
 
1.7%
kohli 466
 
1.7%
yadav 466
 
1.7%
suryakumar 439
 
1.6%
shubman 432
 
1.6%
gill 432
 
1.6%
rahul 432
 
1.6%
Other values (230) 22225
82.5%
2025-06-13T21:03:59.313666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24533
 
14.2%
h 13005
 
7.5%
r 11714
 
6.8%
i 11640
 
6.7%
9746
 
5.7%
n 9172
 
5.3%
e 8623
 
5.0%
s 8385
 
4.9%
l 7423
 
4.3%
u 6128
 
3.6%
Other values (41) 62079
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135902
78.8%
Uppercase Letter 26746
 
15.5%
Space Separator 9746
 
5.7%
Dash Punctuation 53
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24533
18.1%
h 13005
9.6%
r 11714
 
8.6%
i 11640
 
8.6%
n 9172
 
6.7%
e 8623
 
6.3%
s 8385
 
6.2%
l 7423
 
5.5%
u 6128
 
4.5%
t 4925
 
3.6%
Other values (16) 30354
22.3%
Uppercase Letter
ValueCountFrequency (%)
S 5459
20.4%
R 3176
11.9%
P 2761
10.3%
A 2259
8.4%
M 1889
 
7.1%
K 1651
 
6.2%
J 1482
 
5.5%
N 1084
 
4.1%
D 998
 
3.7%
B 915
 
3.4%
Other values (12) 5072
19.0%
Space Separator
ValueCountFrequency (%)
9746
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162648
94.3%
Common 9800
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 24533
15.1%
h 13005
 
8.0%
r 11714
 
7.2%
i 11640
 
7.2%
n 9172
 
5.6%
e 8623
 
5.3%
s 8385
 
5.2%
l 7423
 
4.6%
u 6128
 
3.8%
S 5459
 
3.4%
Other values (38) 56566
34.8%
Common
ValueCountFrequency (%)
9746
99.4%
- 53
 
0.5%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 24533
 
14.2%
h 13005
 
7.5%
r 11714
 
6.8%
i 11640
 
6.7%
9746
 
5.7%
n 9172
 
5.3%
e 8623
 
5.0%
s 8385
 
4.9%
l 7423
 
4.3%
u 6128
 
3.6%
Other values (41) 62079
36.0%

bowler
Text

Distinct128
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size998.1 KiB
2025-06-13T21:04:00.186002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length10.475586
Min length4

Characters and Unicode

Total characters180002
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHazlewood
2nd rowHazlewood
3rd rowHazlewood
4th rowHazlewood
5th rowHazlewood
ValueCountFrequency (%)
sharma 863
 
3.1%
khan 783
 
2.8%
singh 578
 
2.1%
pandya 508
 
1.8%
yadav 506
 
1.8%
kumar 386
 
1.4%
arshdeep 367
 
1.3%
prasidh 364
 
1.3%
siraj 364
 
1.3%
boult 357
 
1.3%
Other values (174) 22480
81.6%
2025-06-13T21:04:01.484992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 28741
16.0%
h 15904
 
8.8%
r 12938
 
7.2%
e 10828
 
6.0%
10373
 
5.8%
n 9574
 
5.3%
i 9134
 
5.1%
s 8589
 
4.8%
d 5391
 
3.0%
l 5373
 
3.0%
Other values (41) 63157
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 142009
78.9%
Uppercase Letter 27620
 
15.3%
Space Separator 10373
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 28741
20.2%
h 15904
11.2%
r 12938
9.1%
e 10828
 
7.6%
n 9574
 
6.7%
i 9134
 
6.4%
s 8589
 
6.0%
d 5391
 
3.8%
l 5373
 
3.8%
u 5141
 
3.6%
Other values (16) 30396
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 4005
14.5%
A 3545
12.8%
K 2683
9.7%
M 1826
 
6.6%
P 1795
 
6.5%
R 1724
 
6.2%
H 1509
 
5.5%
C 1493
 
5.4%
B 1446
 
5.2%
J 1124
 
4.1%
Other values (14) 6470
23.4%
Space Separator
ValueCountFrequency (%)
10373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 169629
94.2%
Common 10373
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 28741
16.9%
h 15904
 
9.4%
r 12938
 
7.6%
e 10828
 
6.4%
n 9574
 
5.6%
i 9134
 
5.4%
s 8589
 
5.1%
d 5391
 
3.2%
l 5373
 
3.2%
u 5141
 
3.0%
Other values (40) 58016
34.2%
Common
ValueCountFrequency (%)
10373
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 28741
16.0%
h 15904
 
8.8%
r 12938
 
7.2%
e 10828
 
6.0%
10373
 
5.8%
n 9574
 
5.3%
i 9134
 
5.1%
s 8589
 
4.8%
d 5391
 
3.0%
l 5373
 
3.0%
Other values (41) 63157
35.1%

runs_of_bat
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.466333
Minimum0
Maximum6
Zeros6253
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-06-13T21:04:01.750617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8102624
Coefficient of variation (CV)1.2345507
Kurtosis0.63430706
Mean1.466333
Median Absolute Deviation (MAD)1
Skewness1.3485842
Sum25196
Variance3.27705
MonotonicityNot monotonic
2025-06-13T21:04:02.026293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6387
37.2%
0 6253
36.4%
4 2251
 
13.1%
6 1297
 
7.5%
2 966
 
5.6%
3 27
 
0.2%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 6253
36.4%
1 6387
37.2%
2 966
 
5.6%
3 27
 
0.2%
4 2251
 
13.1%
5 2
 
< 0.1%
6 1297
 
7.5%
ValueCountFrequency (%)
6 1297
 
7.5%
5 2
 
< 0.1%
4 2251
 
13.1%
3 27
 
0.2%
2 966
 
5.6%
1 6387
37.2%
0 6253
36.4%

extras
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06838154
Minimum0
Maximum5
Zeros16218
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size134.4 KiB
2025-06-13T21:04:02.423668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33728563
Coefficient of variation (CV)4.9324076
Kurtosis93.362626
Mean0.06838154
Median Absolute Deviation (MAD)0
Skewness8.1834437
Sum1175
Variance0.11376159
MonotonicityNot monotonic
2025-06-13T21:04:03.250469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 16218
94.4%
1 878
 
5.1%
2 37
 
0.2%
4 25
 
0.1%
5 24
 
0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 16218
94.4%
1 878
 
5.1%
2 37
 
0.2%
3 1
 
< 0.1%
4 25
 
0.1%
5 24
 
0.1%
ValueCountFrequency (%)
5 24
 
0.1%
4 25
 
0.1%
3 1
 
< 0.1%
2 37
 
0.2%
1 878
 
5.1%
0 16218
94.4%

wide
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
0
16525 
1
 
658

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

Length

2025-06-13T21:04:03.610718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:03.869851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16525
96.2%
1 658
 
3.8%

legbyes
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
0
16975 
1
 
208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

Length

2025-06-13T21:04:04.104224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:04.322940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16975
98.8%
1 208
 
1.2%

byes
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
0
17156 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

Length

2025-06-13T21:04:04.578352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:04.797068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17156
99.8%
1 27
 
0.2%

noballs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
0
17115 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

Length

2025-06-13T21:04:05.156470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:05.375227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17115
99.6%
1 68
 
0.4%

wicket_type
Categorical

Imbalance 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size938.9 KiB
not out
16311 
caught
 
622
bowled
 
133
lbw
 
53
runout
 
39
Other values (4)
 
25

Length

Max length12
Median length7
Mean length6.9430251
Min length3

Characters and Unicode

Total characters119302
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot out
2nd rownot out
3rd rownot out
4th rownot out
5th rowcaught

Common Values

ValueCountFrequency (%)
not out 16311
94.9%
caught 622
 
3.6%
bowled 133
 
0.8%
lbw 53
 
0.3%
runout 39
 
0.2%
stumped 18
 
0.1%
hit wicket 3
 
< 0.1%
retired out 2
 
< 0.1%
retired hurt 2
 
< 0.1%

Length

2025-06-13T21:04:05.641977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:05.933027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
out 16313
48.7%
not 16311
48.7%
caught 622
 
1.9%
bowled 133
 
0.4%
lbw 53
 
0.2%
runout 39
 
0.1%
stumped 18
 
0.1%
retired 4
 
< 0.1%
hit 3
 
< 0.1%
wicket 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 33315
27.9%
o 32796
27.5%
u 17033
14.3%
n 16350
13.7%
16318
13.7%
h 627
 
0.5%
c 625
 
0.5%
a 622
 
0.5%
g 622
 
0.5%
w 189
 
0.2%
Other values (10) 805
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102984
86.3%
Space Separator 16318
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 33315
32.3%
o 32796
31.8%
u 17033
16.5%
n 16350
15.9%
h 627
 
0.6%
c 625
 
0.6%
a 622
 
0.6%
g 622
 
0.6%
w 189
 
0.2%
b 186
 
0.2%
Other values (9) 619
 
0.6%
Space Separator
ValueCountFrequency (%)
16318
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102984
86.3%
Common 16318
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 33315
32.3%
o 32796
31.8%
u 17033
16.5%
n 16350
15.9%
h 627
 
0.6%
c 625
 
0.6%
a 622
 
0.6%
g 622
 
0.6%
w 189
 
0.2%
b 186
 
0.2%
Other values (9) 619
 
0.6%
Common
ValueCountFrequency (%)
16318
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 33315
27.9%
o 32796
27.5%
u 17033
14.3%
n 16350
13.7%
16318
13.7%
h 627
 
0.5%
c 625
 
0.5%
a 622
 
0.5%
g 622
 
0.5%
w 189
 
0.2%
Other values (10) 805
 
0.7%
Distinct149
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size894.7 KiB
2025-06-13T21:04:06.692189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.311296
Min length4

Characters and Unicode

Total characters74081
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.2%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowde Kock
ValueCountFrequency (%)
none 16311
92.2%
sharma 28
 
0.2%
singh 22
 
0.1%
iyer 19
 
0.1%
rahul 18
 
0.1%
nitish 18
 
0.1%
priyansh 17
 
0.1%
ayush 17
 
0.1%
arya 17
 
0.1%
prabhsimran 17
 
0.1%
Other values (203) 1205
 
6.8%
2025-06-13T21:04:07.872218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 16791
22.7%
n 16788
22.7%
o 16530
22.3%
N 16379
22.1%
a 1241
 
1.7%
h 645
 
0.9%
i 605
 
0.8%
r 602
 
0.8%
506
 
0.7%
s 428
 
0.6%
Other values (40) 3566
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55883
75.4%
Uppercase Letter 17686
 
23.9%
Space Separator 506
 
0.7%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16791
30.0%
n 16788
30.0%
o 16530
29.6%
a 1241
 
2.2%
h 645
 
1.2%
i 605
 
1.1%
r 602
 
1.1%
s 428
 
0.8%
l 350
 
0.6%
u 277
 
0.5%
Other values (16) 1626
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
N 16379
92.6%
S 232
 
1.3%
R 170
 
1.0%
P 148
 
0.8%
A 143
 
0.8%
M 94
 
0.5%
K 85
 
0.5%
J 70
 
0.4%
H 49
 
0.3%
D 48
 
0.3%
Other values (12) 268
 
1.5%
Space Separator
ValueCountFrequency (%)
506
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73569
99.3%
Common 512
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16791
22.8%
n 16788
22.8%
o 16530
22.5%
N 16379
22.3%
a 1241
 
1.7%
h 645
 
0.9%
i 605
 
0.8%
r 602
 
0.8%
s 428
 
0.6%
l 350
 
0.5%
Other values (38) 3210
 
4.4%
Common
ValueCountFrequency (%)
506
98.8%
- 6
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16791
22.7%
n 16788
22.7%
o 16530
22.3%
N 16379
22.1%
a 1241
 
1.7%
h 645
 
0.9%
i 605
 
0.8%
r 602
 
0.8%
506
 
0.7%
s 428
 
0.6%
Other values (40) 3566
 
4.8%
Distinct190
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size893.8 KiB
2025-06-13T21:04:08.907840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length4
Mean length4.2568818
Min length4

Characters and Unicode

Total characters73146
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.4%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowJitesh Sharma
ValueCountFrequency (%)
none 16504
93.8%
sharma 40
 
0.2%
singh 27
 
0.2%
jitesh 20
 
0.1%
rickelton 17
 
0.1%
khan 17
 
0.1%
hetmyer 13
 
0.1%
sai 12
 
0.1%
yadav 12
 
0.1%
dhir 12
 
0.1%
Other values (251) 927
 
5.3%
2025-06-13T21:04:10.643009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 16938
23.2%
e 16914
23.1%
o 16668
22.8%
N 16547
22.6%
a 967
 
1.3%
h 535
 
0.7%
i 474
 
0.6%
r 465
 
0.6%
418
 
0.6%
s 350
 
0.5%
Other values (44) 2870
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55056
75.3%
Uppercase Letter 17623
 
24.1%
Space Separator 418
 
0.6%
Other Punctuation 21
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 16938
30.8%
e 16914
30.7%
o 16668
30.3%
a 967
 
1.8%
h 535
 
1.0%
i 474
 
0.9%
r 465
 
0.8%
s 350
 
0.6%
l 269
 
0.5%
t 232
 
0.4%
Other values (15) 1244
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
N 16547
93.9%
S 203
 
1.2%
R 113
 
0.6%
A 96
 
0.5%
P 89
 
0.5%
K 76
 
0.4%
J 75
 
0.4%
M 72
 
0.4%
D 56
 
0.3%
B 51
 
0.3%
Other values (14) 245
 
1.4%
Space Separator
ValueCountFrequency (%)
418
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72679
99.4%
Common 467
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 16938
23.3%
e 16914
23.3%
o 16668
22.9%
N 16547
22.8%
a 967
 
1.3%
h 535
 
0.7%
i 474
 
0.7%
r 465
 
0.6%
s 350
 
0.5%
l 269
 
0.4%
Other values (39) 2552
 
3.5%
Common
ValueCountFrequency (%)
418
89.5%
/ 21
 
4.5%
) 12
 
2.6%
( 12
 
2.6%
- 4
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 16938
23.2%
e 16914
23.1%
o 16668
22.8%
N 16547
22.6%
a 967
 
1.3%
h 535
 
0.7%
i 474
 
0.6%
r 465
 
0.6%
418
 
0.6%
s 350
 
0.5%
Other values (44) 2870
 
3.9%

is_wicket
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size839.1 KiB
1
17183 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17183
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 17183
100.0%

Length

2025-06-13T21:04:10.955513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:11.253936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 17183
100.0%

Most occurring characters

ValueCountFrequency (%)
1 17183
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17183
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17183
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17183
100.0%

over_number
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1482861
Minimum0
Maximum19
Zeros924
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-06-13T21:04:11.530290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6498443
Coefficient of variation (CV)0.617585
Kurtosis-1.1738747
Mean9.1482861
Median Absolute Deviation (MAD)5
Skewness0.045329076
Sum157195
Variance31.920741
MonotonicityNot monotonic
2025-06-13T21:04:11.859206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 924
 
5.4%
4 903
 
5.3%
5 900
 
5.2%
1 900
 
5.2%
3 899
 
5.2%
2 892
 
5.2%
12 889
 
5.2%
7 888
 
5.2%
8 888
 
5.2%
9 887
 
5.2%
Other values (10) 8213
47.8%
ValueCountFrequency (%)
0 924
5.4%
1 900
5.2%
2 892
5.2%
3 899
5.2%
4 903
5.3%
5 900
5.2%
6 885
5.2%
7 888
5.2%
8 888
5.2%
9 887
5.2%
ValueCountFrequency (%)
19 628
3.7%
18 729
4.2%
17 779
4.5%
16 819
4.8%
15 856
5.0%
14 874
5.1%
13 876
5.1%
12 889
5.2%
11 886
5.2%
10 881
5.1%

Interactions

2025-06-13T21:03:49.740839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:44.769089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:45.958097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:47.143690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:48.627822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:49.990844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:45.003462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:46.192505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:47.362482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:48.846519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:50.225217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:45.253467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:46.440565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:47.612305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:49.080901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:50.455848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:45.487631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:46.674979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:47.943278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:49.299651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:50.674550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:45.723759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:46.893691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:48.333895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:03:49.519289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-13T21:04:12.154039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
batting_teambowling_teambyesextrasinningslegbyesmatch_nonoballsoverover_numberruns_of_batstagevenuewicket_typewide
batting_team1.0000.1660.0120.0140.1420.0240.1540.0000.0000.0000.0240.2890.4340.0070.026
bowling_team0.1661.0000.0140.0040.1410.0310.1580.0220.0000.0000.0160.2900.4380.0080.032
byes0.0120.0141.0000.2440.0000.0000.0000.0000.0150.0150.0490.0000.0000.0000.000
extras0.0140.0040.2441.0000.0000.498-0.0020.2630.0310.032-0.2660.0110.0120.0160.831
innings0.1420.1410.0000.0001.0000.0000.0410.0470.0560.0560.0000.0000.0380.0730.000
legbyes0.0240.0310.0000.4980.0001.0000.0000.0000.0240.0240.1450.0080.0180.0140.019
match_no0.1540.1580.000-0.0020.0410.0001.0000.0000.0070.0070.0210.6690.4110.0090.017
noballs0.0000.0220.0000.2630.0470.0000.0001.0000.0150.0150.0000.0000.0150.0000.007
over0.0000.0000.0150.0310.0560.0240.0070.0151.0000.9990.0690.0000.0000.0400.054
over_number0.0000.0000.0150.0320.0560.0240.0070.0150.9991.0000.0700.0000.0000.0400.054
runs_of_bat0.0240.0160.049-0.2660.0000.1450.0210.0000.0690.0701.0000.0110.0240.1210.263
stage0.2890.2900.0000.0110.0000.0080.6690.0000.0000.0000.0111.0000.6250.0080.005
venue0.4340.4380.0000.0120.0380.0180.4110.0150.0000.0000.0240.6251.0000.0140.023
wicket_type0.0070.0080.0000.0160.0730.0140.0090.0000.0400.0400.1210.0080.0141.0000.050
wide0.0260.0320.0000.8310.0000.0190.0170.0070.0540.0540.2630.0050.0230.0501.000

Missing values

2025-06-13T21:03:51.033969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-13T21:03:51.753152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

match_nodatestagevenuebatting_teambowling_teaminningsoverstrikerbowlerruns_of_batextraswidelegbyesbyesnoballswicket_typeplayer_dismissedfielderis_wicketover_number
01Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.1de KockHazlewood000000not outNoneNone10
11Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.2de KockHazlewood400000not outNoneNone10
21Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.3de KockHazlewood000000not outNoneNone10
31Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.4de KockHazlewood000000not outNoneNone10
41Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.5de KockHazlewood000000caughtde KockJitesh Sharma10
51Mar 22, 2025League stageEden Gardens, KolkataKKRRCB10.6RahaneHazlewood000000not outNoneNone10
61Mar 22, 2025League stageEden Gardens, KolkataKKRRCB11.1NarineYash Dayal000000not outNoneNone11
71Mar 22, 2025League stageEden Gardens, KolkataKKRRCB11.2NarineYash Dayal000000not outNoneNone11
81Mar 22, 2025League stageEden Gardens, KolkataKKRRCB11.3NarineYash Dayal000000not outNoneNone11
91Mar 22, 2025League stageEden Gardens, KolkataKKRRCB11.4NarineYash Dayal000000not outNoneNone11
match_nodatestagevenuebatting_teambowling_teaminningsoverstrikerbowlerruns_of_batextraswidelegbyesbyesnoballswicket_typeplayer_dismissedfielderis_wicketover_number
1717374Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB218.3Shashank SinghBhuvneshwar000000not outNoneNone118
1717474Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB218.4Shashank SinghBhuvneshwar400000not outNoneNone118
1717574Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB218.5Shashank SinghBhuvneshwar200000not outNoneNone118
1717674Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB218.6Shashank SinghBhuvneshwar100000not outNoneNone118
1717774Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.1Shashank SinghHazlewood000000not outNoneNone119
1717874Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.2Shashank SinghHazlewood000000not outNoneNone119
1717974Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.3Shashank SinghHazlewood600000not outNoneNone119
1718074Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.4Shashank SinghHazlewood400000not outNoneNone119
1718174Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.5Shashank SinghHazlewood600000not outNoneNone119
1718274Jun 03, 2025Playoffs stageNarendra Modi Stadium, AhmedabadPBKSRCB219.6Shashank SinghHazlewood600000not outNoneNone119